Projects
Bosch Production Line: Predictive Quality Control
End-to-end ML pipeline predicting manufacturing failures on 1.18M rows with 171:1 class imbalance. Engineered path features revealing 72× failure rate signal — certain station paths fail at 41.7% vs 0.58% global mean. Chunk-aware CV and phased feature roadmap progressing from MCC 0.19 → 0.33, targeting ≥ 0.52.
Silent Recalls: Live Vehicle Safety Monitoring
Production-grade ETL pipeline monitoring NHTSA complaints with live risk tracking. Automated detection of vehicles with dangerous complaint-to-recall ratios. GMC Sierra 1500: 445 complaints, zero recalls. Weekly automated runs with hash-based alerting.
Bearing Failure Prediction: 2.88h Accuracy
Production-grade ML system predicting bearing RUL with 2.88-hour accuracy in critical zones. 10x improvement through weighted loss optimization. $300K annual savings, 98.5% failures prevented.
About
I design and build machine learning systems that convert raw, complex data into practical decision tools. My work focuses on developing end-to-end ML pipelines, covering data collection, feature engineering, model development, and automation, with emphasis on predictive systems, risk modeling, and operational intelligence. I’m interested in building systems that anticipate failures, detect risk early, and help organizations act on data with confidence.
Skills
Machine Learning
- TensorFlow & PyTorch
- Computer Vision
- Time Series Analysis
- Predictive Modeling
Engineering
- Mechanical Design
- Manufacturing Processes
- Industrial Automation
- Quality Systems
Data & Analytics
- Python & SQL
- Statistical Analysis
- Data Visualization
- Business Intelligence
Learning Log
CLI Research Agent — Project Deep Dive
A language model that drives a multi-step research workflow autonomously. No frameworks, just direct API calls, a messages array, and a loop. Built to understand how agents actually work at the mechanical level.
Supervised Learning Models — Concept Exploration
A breakdown of the model families you reach for most in production —> linear models, tree-based ensembles, and gradient boosting. Deep dives on each.
Data Preprocessing — Concept Exploration
A systematic series on preparing data for machine learning —> feature scaling, encoding, missing data, and outlier treatment.
California Housing Price Prediction — ML Deep Dive
A hands-on ML learning series covering data loading, EDA, visualization, feature engineering, and stratified sampling — concept by concept.
Spaceship Titanic SQL Case Study
SQL-powered analysis uncovering how CryoSleep dictated passenger outcomes, debunking "planet" and "deck" myths, and revealing one true spatial anomaly.
Computer Vision for Defect Detection
Implementation details of a CNN-based system for detecting microscopic defects in manufactured components.
Graph Neural Networks in Manufacturing
Exploring how GNNs can model complex relationships in supply chain and production line optimization.
Fast.ai Course Notes: Practical Deep Learning
Key lessons and code snippets from completing the Fast.ai practical deep learning course.
Get In Touch
Open to full-time opportunities and collaborative projects.